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Metadata - sample and sequencing assay

  • Sample name: WES_PCB-2021-010-CTD-2021-D-00038
  • Tumor primary site: Soft Tissue
  • Sequencing mode input (VCF): Tumor-Control
  • Sequencing type input (VCF): WES
  • Coding target size (VCF): 34 Mb

Report configuration

The report is generated with PCGR version 0.9.1, using the following key settings:

  • Minimum sequencing depth (DP) tumor (SNV + InDels): 0
  • Minimum allelic fraction (AF) tumor (SNV + InDels): 0
  • Minimum sequencing depth (DP) control (SNV + InDels): 0
  • Maximum allelic fraction (AF) control (SNV + InDels): 1
  • Tier system (VCF): pcgr_acmg
  • MSI prediction: ON
  • Mutational burden estimation: ON
    • TMB algorithm: all_coding
  • Mutational signatures estimation: ON
    • All reference signatures: FALSE
  • Report theme (Bootstrap): default

Main results

Somatic SNVs/InDels

Tumor mutational burden (TMB)

For estimation of TMB, PCGR employs two different approaches/algorithms ( all_coding, and nonsyn, see details outlined in the Documentation below).

TMB algorithm chosen by user: all_coding Size of targeted coding region: 34 Mb

Estimated mutational burden: 0.65 mutations/Mb



TMB reference distributions - TCGA


The plot below indicates how the mutational burden estimated for the query tumor sample (red dotted line) compares with the distributions observed for tumor samples in The Cancer Genome Atlas (TCGA). The grey area indicates the upper TMB tertile as defined by the user. Please note the following characteristics of the TCGA dataset presented here, which must be taken into account during TMB interpretation of the query sample:

  • The TCGA tumor samples are sequenced with a mean coverage of approximately 100X
  • The TCGA somatic mutation calls are based on a consensus among variant callers (each variant is supported by a minimum of two variant calling algorithms)
  • The TCGA somatic mutation calls are based on paired tumor-control sequencing (tumor-only sequencing may produce higher numbers due to more noise)





Tier & variant statistics

  • Number of SNVs: 56
  • Number of InDels: 9
  • Number of protein-coding variants: 19

The prioritization of SNV/InDels is here done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG (Li et al. 2017).

  • Tier 1 - variants of strong clinical significance: 0
  • Tier 2 - variants of potential clinical significance: 0
  • Tier 3 - variants of unknown clinical significance: 3
  • Tier 4 - other coding variants: 16
  • Noncoding variants: 46



Global distribution - allelic support


Global variant browser

The table below permits filtering of the total SNV/InDel set by various criteria.

NOTE 1: The filtering applies to this table only, and not to the tier-specific tables below.

NOTE 2: Filtering on sequencing depth/allelic fraction depends on input specified by user (VCF INFO tags).





Tier 1 - Variants of strong clinical significance



Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


No variant-evidence item associations found.



Diagnostic biomarkers


No variant-evidence item associations found.



Tier 2 - Variants of potential clinical significance

  • Tier 2 considers evidence items of i) strong evidence levels (A & B) in other tumor types, and ii) weak evidence levels (C, D & E) in the query tumor type (Soft Tissue). Using the database for clinical interpretations of variants in cancer (CIViC) and Cancer Biomarkers database, a total of 0 unique, somatic variants were found in the tumor sample:
    • Tier 2 - Predictive/Therapeutic: 0 evidence items
    • Tier 2 - Prognostic: 0 evidence items
    • Tier 2 - Diagnostic: 0 evidence items



Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


No variant-evidence item associations found.



Diagnostic biomarkers


No variant-evidence item associations found.



Tier 3 - Variants of unknown clinical significance

  • A total of 3 unique, somatic variant(s) in the tumor sample are of unknown clinical significance, as found within known proto-oncogenes or tumor suppressor genes.

Tumor suppressor gene mutations


No variants found.



Proto-oncogene mutations


The table below lists all variants:





Tier 4 - Other coding mutations

  • A total of 16 unique, coding somatic variant(s) are also found in the tumor sample.





Noncoding mutations

  • A total of 46 unique, somatic variant(s) that do not alter any encoded protein sequence(s) are also found in the tumor sample.



Complete biomarker set


A list of ALL associations between variants in the tumor sample and known biomarkers are shown in this section, i.e. also listing biomarkers that are not assigned to TIER 1/TIER 2.

NOTE: Reported biomarkers in CIViC/CGI are mapped at different resolutions (i.e. filter Biomarker mapping). The accuracy of a match between variants in the tumor sample and the reported biomarkers will vary accordingly (highlighted by gene symbols with different color backgrounds):

  • Biomarker match at the exact variant/codon level

  • Biomarker match at the exon/gene level


MSI status

Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 98.9% and a negative predictive value of 98.8% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.


  • Predicted MSI status for WES_PCB-2021-010-CTD-2021-D-00038 : MSS (Microsatellite stable)

Supporting evidence: indel fraction among somatic calls

The plot below illustrates the fraction of indels among all calls in WES_PCB-2021-010-CTD-2021-D-00038 (black dashed line) along with the distribution of indel fractions for TCGA samples (colorectal, endometrial, ovarian, stomach) with known MSI status assayed from mononucleotide markers ( MSI.H = high microsatellite instability, MSS = microsatellite stable)



Somatic coding mutations in MSI-associated genes


No variants found.

Mutational signatures

The number of SNV calls (n = 56 ) is too limited. A minimum of 200 SNVs is currently required for a mutational signature analysis to be performed.



Clinical trials (Beta)

  • Ongoing or planned clinical trials in the relevant tumor type have been retrieved from clinicaltrials.gov, focusing on the subset with molecularly targeted therapies
  • Key information entities (interventions/drugs, conditions) in trial records have been mapped to established thesauri (ChEMBL, NCI Thesaurus, UMLS/MedGen)
  • Results from a text-mining procedure on unstructured trial text (e.g. inclusion/exclusion criteria) attempts to highlight the presence of established molecular biomarkers in cancer and relevant therapeutic contexts.



Documentation

Annotation resources

The analysis performed in the cancer genome report is based on the following underlying tools and knowledge resources:

  • Software
    • VEP - Variant Effect Predictor (v101)
    • LOFTEE - Loss-Of-Function Transcript Effect Estimator (VEP plugin) (v1.0.3)
    • vcfanno - Rapid annotation of VCF with other VCFs/BEDs/tabixed files (v0.3.2)
    • MutationalPatterns - Comprehensive genome-wide analysis of mutational processes (v3.0.1)
    • vcf2maf - VCF to MAF conversion (v1.6.18)
    • vcf-validator - Validation suite for Variant Call Format (VCF) files, implemented using C++11 (v0.9.3)
    • vt - A tool set for short variant discovery in genetic sequence data (v0.57721)

  • Databases/datasets
    • GENCODE - high quality reference gene annotation and experimental validation (release 35/19)
    • dbNSFP - Database of non-synonymous functional predictions (20200616 (v4.1))
    • Pfam - Collection of protein families/domains (2020_05 (v33.1))
    • TCGA - The Cancer Genome Atlas - somatic mutations (20201029 (v27))
    • ICGC-PCAWG - ICGC-Pancancer Analysis of Whole Genomes - somatic mutations (2020_01)
    • TCGA-PCDM - Putative Cancer Driver Mutations based on multiple discovery approaches (2019)
    • UniProtKB - Comprehensive resource of protein sequence and functional information (2020_05)
    • CORUM - The comprehensive resource of mammalian protein complexes (20180903 (release 3.0))
    • gnomAD - Germline variant frequencies exome-wide (r2.1 (October 2018))
    • COSMIC - Catalogue of somatic mutations in cancer (90)
    • dbSNP - Database of short genetic variants (153)
    • 1000Genomes - Germline variant frequencies genome-wide (20130502 (phase 3))
    • DisGenet - Database of gene-disease associations (2020_05 (v7.0))
    • DoCM - Database of curated mutations (release 3.2)
    • CancerHotspots - A resource for statistically significant mutations in cancer (2017)
    • ClinVar - Database of genomic variants of clinical significance (20201031)
    • CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (20201112 (release 30))
    • OncoTree - Open-source ontology developed at MSK-CC for standardization of cancer type diagnosis (2020_10_01)
    • DiseaseOntology - Standardized ontology for human disease (20201111)
    • EFO - Experimental Factor Ontology (v3.24)
    • OpenTargetPlatform - Comprehensive and robust data integration for access to potential drug targets associated with disease (2020_09)
    • ChEMBL - Manually curated database of bioactive molecules (20200518 (v27))
    • KEGG - Knowledge base on the molecular interaction, reaction and relation networks (20201120)
    • CIViC - Clinical interpretations of variants in cancer (20201118)
    • CGI - Cancer Genome Interpreter Cancer Biomarkers Database (20180117)

Report content


SNVs/InDels

The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.

  • TIER 1: Variants of strong clinical significance - constitutes variants linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are
    • Found within the same tumor type/class as specified by the user, AND
    • Of strong clinical evidence (i.e. part of guidelines, validated or discovered in late clinical trials (CIViC evidence levels A/B))
    • overlap between variants in the tumor sample and reported biomarkers must occur at the exact variant level or at the codon/exon level
  • TIER 2: Variants of potential clinical significance - constitutes other variants linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are either
    • Of strong clinical evidence in other tumor types/classes than the one specified by the user, OR
    • Of weak clinical evidence (early trials, case reports etc. (CIViC evidence levels C/D/E))) in the same tumor type/class as specified by the user
    • overlap between variants in the tumor sample and reported biomarkers must occur at the exact variant level or at the codon/exon level
  • TIER 3: Variants of uncertain clinical significance - includes other coding variants found in oncogenes or tumor suppressor genes
    • Status as oncogenes and/or tumor suppressors genes are done according to the following scheme in PCGR:
      • Five or more publications in the biomedical literature that suggests an oncogenic/tumor suppressor role for a given gene (as collected from the CancerMine text-mining resource), OR
      • At least two publications from CancerMine that suggests an oncogenic/tumor suppressor role for a given gene AND an existing record for the same gene as a tumor suppressor/oncogene in the Network of Cancer Genes (NCG)
      • Status as oncogene is ignored if a given gene has three times as much (literature evidence) support for a role as a tumor suppressor gene (and vice versa)
      • Oncogenes/tumor suppressor candidates from CancerMine/NCG that are found in the curated list of false positive cancer drivers compiled by Bailey et al. (Cell, 2018) have been excluded
  • TIER 4 - includes other protein-coding variants
    • Protein-coding refers here to variants with the following consequences:
    • missense_variant
    • stop_gain/stop_loss
    • frameshift_variant
    • inframe_insertions/inframe_deletions
    • splice_donor_variant/splice_acceptor_variant
    • start_loss
  • NONCODING - includes other non-(protein)coding variants

A complete list of reported biomarkers that associate with variants in the tumor sample (not necessarily qualifying for assignment to TIER 1/TIER 2) is also shown in a separate section.


Somatic copy number aberrations

Somatic copy number aberrations identified in the tumor sample are classified into two main tiers:

  • TIER 1: Aberrations of strong clinical significance - constitutes amplified/lost genes linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are
    • Found within the same tumor type/class as specified by the user, AND
    • Of strong clinical evidence (i.e. part of guidelines, validated or discovered in late clinical trials (CIViC evidence levels A/B))
  • TIER 2: Aberrations of potential clinical significance - constitutes amplified/lost genes linked to predictive, prognostic, or diagnostic biomarkers in the CIViC database and the Cancer Biomarkers Database that are either
    • Of strong clinical evidence in other tumor types/classes than the one specified by the user, OR
    • Of weak clinical evidence (early trials, case reports etc. (CIViC evidence levels C/D/E))) in the same tumor type/class as specified by the user

Included in the report is also a complete list of all oncogenes subject to amplifications, tumor suppressor genes subject to homozygous deletions, and other drug targets subject to amplifications


Mutational signatures

The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation. Mutational signatures can have significant clinical impact in certain tumor types (Póti et al., 2019, Ma et al., 2018)

The MutationalPatterns package (Blokzijl et al., 2018) is used to estimate the relative contribution of known mutational signatures in a single tumor sample. MutationalPatterns makes an optimal reconstruction of the mutations observed in a given sample with a reference collection of n = 67 mutational signatures. By default, we restrict the signatures in the reference collection to those already observed in the tumor type in question (i.e. from large-scale de novo signature extraction on ICGC tumor samples).

NOTE: This sample contains too few variants for estimation of mutational signature contributions


Tumor mutational burden (TMB)

Tumor mutational load or mutational burden is a measure of the number of mutations within a tumor genome, defined as the total number of mutations per coding area of a tumour genome. TMB may serve as a proxy for determining the number of neoantigens per tumor, which in turn may have implications for response to immunotherapy. For estimation of TMB, PCGR employs two different algorithms (one to be chosen by the user):

  1. all_coding: the same approach as was outlined in a recently published large-scale study of TMB (Chalmers et al., 2017), i.e. counting all somatic base substitutions and indels in the protein-coding regions of the sequencing assay, including synonymous alterations.
  2. nonsyn: non-synonymous variants only, i.e. as employed by Fernandez et al., 2019

Numbers obtained with 1) or 2) is next divided by the coding target size of the sequencing assay.


MSI classification

Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 100% and a negative predictive value of 99.4% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.

Note that the MSI classifier is applied only for WGS/WES tumor-control sequencing assays.


Kataegis

Kataegis describes a pattern of localized hypermutations identified in some cancer genomes, in which a large number of highly-patterned basepair mutations occur in a small region of DNA (ref Wikipedia). Kataegis is prevalently seen among breast cancer patients, and it is also exists in lung cancers, cervical, head and neck, and bladder cancers, as shown in the results from tracing APOBEC mutation signatures (ref Wikipedia). PCGR implements the kataegis detection algorithm outlined in the KataegisPortal R package.

Explanation of key columns in the resulting table of potential kataegis events:

  • weight.C>X: proportion of C>X mutations
  • confidence: confidence degree of potential kataegis events (range: 0 to 3)
    • 0 - a hypermutation with weight.C>X < 0.8;
    • 1 - one hypermutation with weight.C>X >= 0.8 in a chromosome;
    • 2 - two hypermutations with weight.C>X >= 0.8 in a chromosome;
    • 3 - high confidence with three or more hypermutations with weight.C>X >= 0.8 in a chromosome)

References

Alexandrov, Ludmil B, Jaegil Kim, Nicholas J Haradhvala, Mi Ni Huang, Alvin Wei Tian Ng, Yang Wu, Arnoud Boot, et al. 2020. “The Repertoire of Mutational Signatures in Human Cancer.” Nature 578 (7793): 94–101. http://dx.doi.org/10.1038/s41586-020-1943-3.
Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Samuel A J R Aparicio, Sam Behjati, Andrew V Biankin, Graham R Bignell, et al. 2013. “Signatures of Mutational Processes in Human Cancer.” Nature 500 (7463): 415–21. https://doi.org/10.1038/nature12477.
Bailey, Matthew H, Collin Tokheim, Eduard Porta-Pardo, Sohini Sengupta, Denis Bertrand, Amila Weerasinghe, Antonio Colaprico, et al. 2018. “Comprehensive Characterization of Cancer Driver Genes and Mutations.” Cell 173 (2): 371–385.e18. http://dx.doi.org/10.1016/j.cell.2018.02.060.
Blokzijl, Francis, Roel Janssen, Ruben van Boxtel, and Edwin Cuppen. 2018. “MutationalPatterns: Comprehensive Genome-Wide Analysis of Mutational Processes.” Genome Med. 10 (1): 33. http://dx.doi.org/10.1186/s13073-018-0539-0.
Chalmers, Zachary R, Caitlin F Connelly, David Fabrizio, Laurie Gay, Siraj M Ali, Riley Ennis, Alexa Schrock, et al. 2017. “Analysis of 100,000 Human Cancer Genomes Reveals the Landscape of Tumor Mutational Burden.” Genome Med. 9 (1): 34. https://doi.org/10.1186/s13073-017-0424-2.
Cortes-Ciriano, Isidro, Sejoon Lee, Woong-Yang Park, Tae-Min Kim, and Peter J Park. 2017. “A Molecular Portrait of Microsatellite Instability Across Multiple Cancers.” Nat. Commun. 8: 15180. https://doi.org/10.1038/ncomms15180.
Fernandez, Evan M, Kenneth Eng, Shaham Beg, Himisha Beltran, Bishoy M Faltas, Juan Miguel Mosquera, David M Nanus, et al. 2019. “Cancer-Specific Thresholds Adjust for Whole Exome Sequencing–Based Tumor Mutational Burden Distribution.” JCO Precision Oncology, no. 3 (December): 1–12. https://doi.org/10.1200/PO.18.00400.
Griffith, Malachi, Nicholas C Spies, Kilannin Krysiak, Joshua F McMichael, Adam C Coffman, Arpad M Danos, Benjamin J Ainscough, et al. 2017. “CIViC Is a Community Knowledgebase for Expert Crowdsourcing the Clinical Interpretation of Variants in Cancer.” Nat. Genet. 49 (2): 170–74. http://dx.doi.org/10.1038/ng.3774.
Lever, Jake, Eric Y Zhao, Jasleen Grewal, Martin R Jones, and Steven J M Jones. 2019. “CancerMine: A Literature-Mined Resource for Drivers, Oncogenes and Tumor Suppressors in Cancer.” Nat. Methods 16 (6): 505–7. http://dx.doi.org/10.1038/s41592-019-0422-y.
Li, Marilyn M, Michael Datto, Eric J Duncavage, Shashikant Kulkarni, Neal I Lindeman, Somak Roy, Apostolia M Tsimberidou, et al. 2017. “Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists.” J. Mol. Diagn. 19 (1): 4–23. https://dx.doi.org/10.1016.
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Póti, Ádám, Hella Gyergyák, Eszter Németh, Orsolya Rusz, Szilárd Tóth, Csenger Kovácsházi, Dan Chen, et al. 2019. “Correlation of Homologous Recombination Deficiency Induced Mutational Signatures with Sensitivity to PARP Inhibitors and Cytotoxic Agents.” Genome Biol. 20 (1): 240. http://dx.doi.org/10.1186/s13059-019-1867-0.
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